Data Clustering Recommendations For Selection Student Majors To Higher Edication Using The K-Means Method (Case Study of SMAN 2 Palembang) - Jemakmun makmun Jemakmun; R. Ahmad Dicky Syarief Purboyo (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2023)
SMA Negeri 2 Palembang has two majors, science and social studies. As a result of choosing the wrong major after entering college, students sometimes experience difficulties and feel the wrong major, in connection with this problem the author tries to provide a solution for determining majors for college using the k-means clustering method. In this study, the students were grouped using the data mining method. The group is based on the attributes of majors, interests, traits, hobbies, talents, and the average value of science and social science subjects. Clustering data using the K-Means method and measuring the Euclidean distance, analyzed using Microsoft excel manual calculations and RapidMiner tools. The results of the study indicate that Cluster 1 is a cluster that is recommended to take the Language major. Cluster 2 is a cluster that is recommended for a major in Engineering. Cluster 3 is a cluster that is recommended to major in Health/Medicine. Cluster 4 is a cluster that is recommended for majoring in Economics. Cluster 5 is the recommended cluster for majoring in Language. While the results of the calculation research using RapidMiner, Cluster_0 is recommended to major in Engineering, Cluster_1 is recommended to major in Economics, Cluster_2 is recommended to major in Language, Cluster_3 is recommended to major in Education, Cluster_4 is recommended to major in Medicine/Health.